docs: def plot_all(self, fig=None, iabscissa=1, iteridx=None,
               foffset=1e-19, x_opt=None, fontsize=9):
          """
          plot data from a `CMADataLogger` (using the files written by the logger).
  
          Arguments
          ---------
              `fig`
                  figure number, by default 425
              `iabscissa`
                  ``0==plot`` versus iteration count,
                  ``1==plot`` versus function evaluation number
              `iteridx`
                  iteration indices to plot
  
          Return `CMADataLogger` itself.
  
          Examples
          --------
          ::
  
              import cma
              logger = cma.CMADataLogger()  # with default name
              # try to plot the "default logging" data (e.g.
              #   from previous fmin calls, which is essentially what
              #   also cma.plot() does)
              logger.plot_all()
              cma.savefig('fig425.png')  # save current figure
              logger.closefig()
  
          Dependencies: matlabplotlib/pyplot.
  
          """
          try:
              # pyplot: prodedural interface for matplotlib
              from  matplotlib.pyplot import figure, subplot, gcf
          except ImportError:
              ImportError('could not find matplotlib.pyplot module, function plot() is not available')
              return
  
          if fig is None:
              fig = 426
          if iabscissa not in (0, 1):
              iabscissa = 1
  
          self.load()
          dat = self
  
          # index out some data
          if iteridx is not None:
              self.select_data(iteridx)
  
          if len(dat.f) == 0:
              print('nothing to plot')
              return
  
          # not in use anymore, see formatter above
          # xticklocs = np.arange(5) * np.round(minxend/4., -int(np.log10(minxend/4.)))
  
          # dfit(dfit<1e-98) = NaN;
  
          # TODO: if abscissa==0 plot in chunks, ie loop over subsets where dat.f[:,0]==countiter is monotonous
  
          figure(fig)
          self._enter_plotting(fontsize)
          self.fighandle = gcf()  # fighandle.number
  
          if 1 < 3:
              subplot(2, 3, 1)
              self.plot_divers(iabscissa, foffset)
              pyplot.xlabel('')
  
              # standard deviations
              subplot(2, 3, 4)
              self.plot_stds(iabscissa)
  
              # Scaling
              subplot(2, 3, 2)
              self.plot_axes_scaling(iabscissa)
              pyplot.xlabel('')
  
              # spectrum of correlation matrix
              subplot(2, 3, 5)
              self.plot_correlations(iabscissa)
  
              # x-vectors
              subplot(2, 3, 3)
              self.plot_xrecent(iabscissa, x_opt)
              pyplot.xlabel('')
  
              subplot(2, 3, 6)
              self.plot_mean(iabscissa, x_opt)
  
          self._finalize_plotting()
          return self